Christine Oure

Results-driven Data Engineer with over 2.5 years of experience, skilled in SQL and Python. Successfully engineered a data integration framework, enhancing data accessibility for analytics. Proven ability to lead projects, improve system efficiency, and support data-driven decision-making through robust ETL pipelines and advanced data modeling.

Data Scientist

Linda Kamau
Co-Founder & Director, AkiraChix
📧 lkamau@akirachix.com

James Mwai
CTO, Sky.Garden
📧 smartemwa@gmail.com

Peter Gachuki

Stanford Certified Machine Learning Engineer

📧 peter@emobilis.org

Handwritten Digit Classifier: A Deep Learning Approach to Image Classification

• Developed a Convolutional Neural Network (CNN) for handwritten digit classification using Python, OpenCV (image preprocessing), TensorFlow, and Keras on the MNIST dataset.
• Achieved 97.5% accuracy by implementing data augmentation techniques to enhance model robustness.

Movie Review Sentiment Analyzer: An NLP-Driven Approach to Text Classification

• Developed a Logistic Regression-based sentiment analysis model using Python and NLTK on the IMDb movie review dataset, achieving 88% accuracy in classifying reviews.
• Enhanced model performance through NLP preprocessing, including tokenization, stop word removal, lemmatization, and TF-IDF vectorization for effective feature extraction.

Customer Segmentation using Unsupervised Learning: Identifying Key Customer Groups

• Applied unsupervised learning techniques (K-Means clustering) using Python and scikit-learn on an e-commerce dataset to identify distinct customer segments based on purchasing behavior.
• Evaluated the optimal number of clusters using the silhouette score and visualized the segments using Matplotlib to derive actionable insights into customer groupings.